Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models
Introduction: Iron deficiency anemia is the most common cause of anemia worldwide, and increased iron requirement during pregnancy increases the risk of anemia. Anemia in pregnancy is associated with adverse pregnancy outcomes such as low birth weight, preterm and intrauterine growth restriction. Th...
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| Format: | Article |
| Language: | English |
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SERNEV
2023-12-01
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| Series: | Family Practice and Palliative Care |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3351068 |
| _version_ | 1797244631397695488 |
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| author | Bilal Alatas Burkay Yakar Rüveyda Yıldırım Mehmet Onur Kaya |
| author_facet | Bilal Alatas Burkay Yakar Rüveyda Yıldırım Mehmet Onur Kaya |
| author_sort | Bilal Alatas |
| collection | DOAJ |
| description | Introduction: Iron deficiency anemia is the most common cause of anemia worldwide, and increased iron requirement during pregnancy increases the risk of anemia. Anemia in pregnancy is associated with adverse pregnancy outcomes such as low birth weight, preterm and intrauterine growth restriction. This study used a Rule-based Intelligent Classification Models to predict socio-demographic, nutritional, antenatal care and obstetric factors on iron deficiency anemia during pregnancy
Methods: This retrospective study was a secondary analysis of a community-based cross-sectional study conducted between January and June 2019 in the province of Elazig in eastern Turkey. Data of 495 pregnant women were included in the study iron deficiency anemia was defined as hemoglobin lt; 11 g/dl, and ferritin lt; 30 µg/L. Rule-based machine learning methods were used to predict factors associated with anemia during pregnancy.
Results: The mean age of 495 pregnant women were 30.06 ± 5.15 years. The prevalence of anemia was 27.9% in study population. Maternal age, educational status, occupation, nutrition education status, nutritional property, gravida, and parity were significantly related to anemia. Jrip, OneR, and PART algorithms estimated factors associated with anemia with 96.36%, 85.45%, and 97.98% accuracy, respectively.
Conclusion: Rule-based machine learning algorithm may offer a new approach to risk factors for iron deficiency anemia during pregnancy. With the use of this model, it is possible to predict the risk of anemia both before and during pregnancy and to take preventative measures. |
| first_indexed | 2024-03-08T18:58:55Z |
| format | Article |
| id | doaj.art-67d5747563334710bde739eecafbc655 |
| institution | Directory Open Access Journal |
| issn | 2459-1505 |
| language | English |
| last_indexed | 2024-04-24T19:14:05Z |
| publishDate | 2023-12-01 |
| publisher | SERNEV |
| record_format | Article |
| series | Family Practice and Palliative Care |
| spelling | doaj.art-67d5747563334710bde739eecafbc6552024-03-26T08:15:56ZengSERNEVFamily Practice and Palliative Care2459-15052023-12-018615416410.22391/fppc.1347373544Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification modelsBilal Alatas0Burkay Yakar1Rüveyda Yıldırım2Mehmet Onur Kaya3Department of Software Engineering, Faculty of Engineering, Firat University, ElazığDepartment of Family Medicine, Faculty of Medicine, Firat University, ElazığDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Firat University, ElazığDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Firat University, ElazığIntroduction: Iron deficiency anemia is the most common cause of anemia worldwide, and increased iron requirement during pregnancy increases the risk of anemia. Anemia in pregnancy is associated with adverse pregnancy outcomes such as low birth weight, preterm and intrauterine growth restriction. This study used a Rule-based Intelligent Classification Models to predict socio-demographic, nutritional, antenatal care and obstetric factors on iron deficiency anemia during pregnancy Methods: This retrospective study was a secondary analysis of a community-based cross-sectional study conducted between January and June 2019 in the province of Elazig in eastern Turkey. Data of 495 pregnant women were included in the study iron deficiency anemia was defined as hemoglobin lt; 11 g/dl, and ferritin lt; 30 µg/L. Rule-based machine learning methods were used to predict factors associated with anemia during pregnancy. Results: The mean age of 495 pregnant women were 30.06 ± 5.15 years. The prevalence of anemia was 27.9% in study population. Maternal age, educational status, occupation, nutrition education status, nutritional property, gravida, and parity were significantly related to anemia. Jrip, OneR, and PART algorithms estimated factors associated with anemia with 96.36%, 85.45%, and 97.98% accuracy, respectively. Conclusion: Rule-based machine learning algorithm may offer a new approach to risk factors for iron deficiency anemia during pregnancy. With the use of this model, it is possible to predict the risk of anemia both before and during pregnancy and to take preventative measures.https://dergipark.org.tr/en/download/article-file/3351068gebelikdemir eksikliği anemisialgoritmalarmakine öğrenmepregnancyiron-deficiency anemiaalgorithmsmachine learning |
| spellingShingle | Bilal Alatas Burkay Yakar Rüveyda Yıldırım Mehmet Onur Kaya Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models Family Practice and Palliative Care gebelik demir eksikliği anemisi algoritmalar makine öğrenme pregnancy iron-deficiency anemia algorithms machine learning |
| title | Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models |
| title_full | Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models |
| title_fullStr | Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models |
| title_full_unstemmed | Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models |
| title_short | Analyzing of iron-deficiency anemia in pregnancy using rule-based intelligent classification models |
| title_sort | analyzing of iron deficiency anemia in pregnancy using rule based intelligent classification models |
| topic | gebelik demir eksikliği anemisi algoritmalar makine öğrenme pregnancy iron-deficiency anemia algorithms machine learning |
| url | https://dergipark.org.tr/en/download/article-file/3351068 |
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